Natural science has been a field of active research for ages with new researches being conducted on a regular basis. Each new discovery results in unveiling of new characteristics of known or unknown environmental elements sometimes leading to renaming or evolution of new scientific terms related to environmental elements, such as genomes, proteins, and chemicals. For instance, study of a particular organism's genome, such as human genome and mouse genome, is a field of active research today. An understanding of genome variations may enable researchers to fully understand the issues of genetic susceptibility and pharmacogenomics of drug response for all individuals as well as personalized molecular diagnostic tests. Thus, a vast amount of biomedical literature related to genomic research has been published to assist the researchers in their work. However, in order to use the data, for example, while formulating a new hypothesis or to interpret experimental results, a researcher may need to go through the vast biomedical literature. Studying such a huge volume of data is often a cumbersome and time consuming task, and therefore data mining tools may be implemented. For example, in order to formulate new hypothesis for a particular organism's genome, a researcher may need to identify and extract data related to various genes associated with the particular genome.
Conventionally, researchers followed a process of pattern identification to identify gene data, i.e., data related to a particular gene associated with a specific genome. Pattern identification required a researcher to identify gene data related to a particular gene using a gene pattern associated with the gene, by extracting from various literature sources all documents having the gene pattern. Documents containing the gene pattern may then be studied to identify and use the gene data contained in the documents. For example, during studies related to, say, tumors in human, the researchers may need to identify data related to a tumor suppressor protein that in humans is encoded by a TP53 gene. In such a case using the conventional process of pattern recognition would require the researchers to use a gene pattern of the TP53 gene to access all documents that contain the gene pattern and study the same to identify the gene data related to the TP53 gene. However, owing to the long length of the gene patterns, using such a method of gene data identification may not be efficient in terms of time and resource requirements.
In recent years, various named entity recognition techniques have been implemented to search scientific data, such as protein data related to various proteins, gene data related to a particular gene, chemical and drug data related to a particular chemical or drug, based on a scientific term, such as gene name of the gene. Searching scientific data based on the scientific term reduces the time and sources required for the search as the scientific terms are typically smaller and simpler to search than the conventional methods. For example, searching gene data based on the gene name reduces the time and sources required for the search as the gene names are typically smaller and simpler to search than the conventional methods using the gene patterns. However, identifying a gene based on the gene name may not be feasible owing to various reasons, such as no fixed nomenclature for naming a gene. In absence of a common nomenclature, different researchers may use different gene names to refer to a particular gene while publishing white papers or storing gene data related to the particular gene in a gene database. For instance, a person may name a gene, she researched about, using her birth date, while another person may name the same gene on his own name. Searching gene data based on the gene names may thus require either complex text mining tools or manual intervention to filter and identify various gene names related to a particular gene. Searching other scientific data based on the scientific term may not be feasible due to similar complexities involved.